Improving information filtering via network manipulation

Recommender system is a very promising way to address the problem of overabundant information for online users. Though the information filtering for the online commercial systems received much attention recently, almost all of the previous works are dedicated to design new algorithms and consider the user-item bipartite networks as given and constant information. However, many problems for recommender systems such as the cold-start problem (i.e. low recommendation accuracy for the small degree items) are actually due to the limitation of the underlying user-item bipartite networks. In this letter, we propose a strategy to enhance the performance of the already existing recommendation algorithms by directly manipulating the user-item bipartite networks, namely adding some virtual connections to the networks. Numerical analyses on two benchmark data sets, MovieLens and Netflix, show that our method can remarkably improve the recommendation performance. Specifically, it not only improve the recommendations accuracy (especially for the small degree items), but also help the recommender systems generate more diverse and novel recommendations.Comment: 6 pages, 5 figure

A Comparative Study of Recommendation Systems

Recommendation Systems or Recommender Systems have become widely popular due to surge of information at present time and consumer centric environment. Researchers have looked into a wide range of recommendation systems leveraging a wide range of algorithms. This study investigates three popular recommendation systems in existence, Collaborative Filtering, Content-Based Filtering, and Hybrid recommendation system. The famous MovieLens dataset was utilized for the purpose of this study. The evaluation looked into both quantitative and qualitative aspects of the recommendation systems. We found that from both the perspectives, the hybrid recommendation system performs comparatively better than standalone Collaborative Filtering or Content-Based Filtering recommendation syste

Behavior patterns of online users and the effect on information filtering

Understanding the structure and evolution of web-based user-object bipartite networks is an important task since they play a fundamental role in online information filtering. In this paper, we focus on investigating the patterns of online users' behavior and the effect on recommendation process. Empirical analysis on the e-commercial systems show that users have significant taste diversity and their interests for niche items highly overlap. Additionally, recommendation process are investigated on both the real networks and the reshuffled networks in which real users' behavior patterns can be gradually destroyed. Our results shows that the performance of personalized recommendation methods is strongly related to the real network structure. Detail study on each item shows that recommendation accuracy for hot items is almost maximum and quite robust to the reshuffling process. However, niche items cannot be accurately recommended after removing users' behavior patterns. Our work also is meaningful in practical sense since it reveals an effective direction to improve the accuracy and the robustness of the existing recommender systems.Comment: 8 pages, 6 figure

The reinforcing influence of recommendations on global diversification

Recommender systems are promising ways to filter the overabundant information in modern society. Their algorithms help individuals to explore decent items, but it is unclear how they allocate popularity among items. In this paper, we simulate successive recommendations and measure their influence on the dispersion of item popularity by Gini coefficient. Our result indicates that local diffusion and collaborative filtering reinforce the popularity of hot items, widening the popularity dispersion. On the other hand, the heat conduction algorithm increases the popularity of the niche items and generates smaller dispersion of item popularity. Simulations are compared to mean-field predictions. Our results suggest that recommender systems have reinforcing influence on global diversification.Comment: 6 pages, 6 figure

Implementasi dan Analisa Effective Missing Data Prediction pada Collaborative Filtering Recommender System

ABSTRAKSI: Recommender system adalah sistem yang dapat digunakan untuk memprediksi sebuah items dalam hal ini berupa movie, berdasarkan informasi yang diperoleh dari user, sehingga didapatkan rekomendasi berdasarkan profil penggunanya. Collaborative filtering adalah sebuah metoda dari recommender system yang memprediksi suatu item (movie) berdasarkan informasi yang sudah ada dari user atau item lainnya. Untuk mendapatkan hasil prediksi yang maksimal dapat dihasilkan dengan perhitungan similarity baik dari user maupun dari item.Tugas akhir ini menganalisis akurasi prediksi rating yang dihasilkan oleh recommender system setelah mengimplementasikan algoritma effective missing data prediction collaborative filtering. Dimana dalam mendapatkan nilai prediksi dari item item yang belum di rating ini berdasarkan penghitungan dari similarity dari user dan item, beserta menggunakan teknik pembobotan significance weighting. Data yang digunakan adalah data set IMDB(Internet Movie Data Base). Parameter yang digunakan dalam analisis adalah parameter Gamma ,tao, theta, etha dan lambda. Tugas akhir ini menganalisa tingkat akurasi prediksi rating yang dihasilkan dengan metoda evaluasi MAE (Mean Absolut Error)Akurasi prediksi yang dihasilkan oleh algoritma effective missing data prediction collaborative filtering lebih baik dibandingkan dengan classic collaborative filtering. Performansi terbaik terjadi pada saat memprediksi missing data dengan menggunakan informasi dari user maupun item.Kata Kunci : recommender system, collaborative filtering, similarity, missing valueABSTRACT: Recommender system is a system that can be used to predict the items in this case a movie, based on information obtained from users, so get recommendations based on user profiles. Collaborative filtering is a method of recommender systems that predict an item (movie) based on existing information from users or other items. To get the maximum prediction calculation of similarity is required either from user or from the item.This final rating analyze prediction accuracy generated by the recommender system after implementing effevtive missing data prediction algorithm collaborative filtering. Where in obtaining the predicted value of the items items that have not been in the rating is based on the calculation of the similarity of users and items, along with significance weighting weighting technique. The data used is the data set of IMDB (Internet Movie Data Base). The parameters used in the analysis is the parameter Gamma, tao, theta, Ethan and lambda. This final project will analyze the level of prediction accuracy ratings generated by the evaluation method of MAE (Mean Absolute Error)Prediction accuracy generated by the missing data prediction algorithm effevtive collaborative filtering is better than classic collaborative filtering. Best performance occurs when predicting the missing data by using information from the user or item.Keyword: recommender systems, collaborative filtering, similarity, missing valu